The document defines and discusses the key characteristics of data quality: accuracy, precision, relevance, completeness, consistency, transparency, and timeliness. It provides examples to illustrate each characteristic, defining them as the degree to which data matches reality (accuracy), the specificity of data values (precision), how closely data meets the needs of its consumers (relevance), how fully the needs of consumers are met (completeness), how synchronized data is across systems (consistency), the ability to trace data back to its origin (transparency), and the availability of data when it is needed (timeliness).

1. Data Quality Management Definitions The Characteristics of Data Quality

2. What is „Data Quality“? Slide Data Quality stands for: Data Quality Characteristics Accurate Precise Relevant Complete Harmonized information need and provision 1 Mutual understanding of data capability 2 Trustworthy and credible information 3 Consistent Timely Transparent

3. The Characteristic „Accuracy“ Slide Accuracy stands for: Examples for Data Accuracy issues: Data Accuracy is the degree at which a data object overlaps with the real world object or event described. Data accuracy is measured as reciprocal maximum gap between data and reality. [ high is good ] Frank Meyer is recorded as “Fritz Meier” in the Database. An incident is reported with €23m when the loss was €12k. The amount invoiced does not represent the customer’s usage. Accurate Good fit between the data and reality The ability to draw correct conclusions from data Business processes that match reality

4. The Characteristic „Precision“ Slide Precision stands for: Examples of Data Precision issues: Data Precision is the closeness between all possible interpretations of a data object. Data precision is measured as reciprocal maximum distance between all applicable data interpretations. [ high is good ] A close link between desired and offered information The ability to pinpoint decisions based on data. Lean Business processes. Frank Meyer lives in Bonn - or Cologne? Or was that Jon Myers? This Billing incident was caused by Mediation... I think… Why do we charge the customer 2 minutes for a 59sec call? Precise

5. The Characteristic „Relevance“ Slide Relevance stands for: Examples of Data Relevance Issues: Data Relevance is the closeness between data consumer need and data provider output. Data relevance is measured as percentage of all data required divided by all data provided. [100% is best ] Data that helps you know what you want. The ability to use data with maximum efficiency. Not having to sort through information you don’t need. The Revenue Assurance report also tells you about the weather! A CSR asks the cell phone customer if they have a microwave. You need to fill in a 7-page form to apply for a tariff change. Relevant

6. The Characteristic „Accuracy“ Slide Completeness stands for: Examples of Data Completeness issues: Data Completeness is the extent by which the data consumer’s need is met. Data completeness is measured as percentage of data available divided by the data required. [100% is best ] Data that does not leave any open questions. The ability to make a good decision based on available data. Closeness between “need to know” and what the data tells you. We can not tell how many cell phone contracts Egon Huber has. The CC application does not provide a “Call back wanted” field. A summary report includes projects that did not report status! Complete

7. The Characteristic „Consistency“ Slide Consistency stands for: Examples of Data Consistency Issues: Data Consistency is the synchronization of data objects across the company. Data consistency is measured as reciprocal ratio of distinct data objects per described object or event. [100% is best ] Data in harmony across the company. The ability to trust in data regardless of source. Identical information available to all processes and units. We send Mr. Smith’s invoices to “Smith” and ads to “Schmitz”. Asking DWH or SAP for revenue yields different numbers. Mr. Kim defines “churn” as cancel/total and Mr. Jones as cancel/new . Consistent

8. The Characteristic „Transparency“ Slide Transparency stands for: Examples of Data Transparency issues: Data Transparency is the ability to trace back data to it’s origin and find out it’s real world meaning. Data transparency is measured as percentage of maximum traceable distance by total processing steps. [100% is best ] Trustworthy data in the entire data supply chain. The ability to connect data with it’s real meaning. Real accountability for data objects. We can’t tell why Frank Müller is now “Udo Huber” in the DB! A report contains a figure which nobody can explain. Project leaders get away with reporting “green” when it’s “red”! Transparent

9. The Characteristic „Timeliness“ Slide Timeliness stands for: Examples of Data Timeliness Issues: Data that is available without delay. The ability to know what you need, when you need. Smooth Information Flow: ‘Data Delayed’ is ‘Data Denied’! The agenda is distributed during the Telco! Customers decide for a competitor before credit is approved! Receiving a “budget exceeded” SMS after you went over the limit! Timely Data Timeliness is the availability of data at the time it needs to be utilized. Data timeliness is measured as percentage of processing time attributed to waiting for data. [0% is best ]